{
 "cells": [
  {
   "cell_type": "code",
   "execution_count": 1,
   "id": "czech-musical",
   "metadata": {},
   "outputs": [],
   "source": [
    "import tensorflow as tf\n",
    "import numpy as np"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 2,
   "id": "rental-possible",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "tensorflow version:2.4.0\n"
     ]
    }
   ],
   "source": [
    "print(\"tensorflow version:{}\".format(tf.__version__))"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 3,
   "id": "forty-worker",
   "metadata": {},
   "outputs": [],
   "source": [
    "import pandas as pd"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 4,
   "id": "shaped-component",
   "metadata": {},
   "outputs": [],
   "source": [
    "data = pd.read_csv(\"./打乱的数据.csv\")"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 5,
   "id": "aboriginal-pulse",
   "metadata": {
    "scrolled": true
   },
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<style scoped>\n",
       "    .dataframe tbody tr th:only-of-type {\n",
       "        vertical-align: middle;\n",
       "    }\n",
       "\n",
       "    .dataframe tbody tr th {\n",
       "        vertical-align: top;\n",
       "    }\n",
       "\n",
       "    .dataframe thead th {\n",
       "        text-align: right;\n",
       "    }\n",
       "</style>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>kfxs</th>\n",
       "      <th>mklz</th>\n",
       "      <th>csd</th>\n",
       "      <th>pjcd</th>\n",
       "      <th>zqd</th>\n",
       "      <th>dqzs</th>\n",
       "      <th>dlbqd</th>\n",
       "      <th>fsl</th>\n",
       "      <th>hd</th>\n",
       "      <th>mj</th>\n",
       "      <th>scdlqd</th>\n",
       "      <th>sjhs</th>\n",
       "      <th>xscvm</th>\n",
       "      <th>xscvb</th>\n",
       "      <th>xi40</th>\n",
       "      <th>xi50</th>\n",
       "      <th>cu50</th>\n",
       "      <th>xsmj140</th>\n",
       "      <th>xsmj200</th>\n",
       "      <th>xshz</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>148.70</td>\n",
       "      <td>4.33</td>\n",
       "      <td>0.93</td>\n",
       "      <td>29.16</td>\n",
       "      <td>82.91</td>\n",
       "      <td>9.40</td>\n",
       "      <td>32.58</td>\n",
       "      <td>76.75</td>\n",
       "      <td>8.48</td>\n",
       "      <td>247.70 \\r</td>\n",
       "      <td>14.0</td>\n",
       "      <td>18.5</td>\n",
       "      <td>12.82</td>\n",
       "      <td>3.2</td>\n",
       "      <td>83.0</td>\n",
       "      <td>1.0</td>\n",
       "      <td>39.5</td>\n",
       "      <td>282.0</td>\n",
       "      <td>80.0</td>\n",
       "      <td>5.10</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>136.01</td>\n",
       "      <td>4.52</td>\n",
       "      <td>0.89</td>\n",
       "      <td>28.92</td>\n",
       "      <td>82.99</td>\n",
       "      <td>6.70</td>\n",
       "      <td>30.46</td>\n",
       "      <td>68.67</td>\n",
       "      <td>8.87</td>\n",
       "      <td>204.25 \\r</td>\n",
       "      <td>13.2</td>\n",
       "      <td>14.6</td>\n",
       "      <td>12.61</td>\n",
       "      <td>2.4</td>\n",
       "      <td>62.3</td>\n",
       "      <td>0.8</td>\n",
       "      <td>31.8</td>\n",
       "      <td>213.8</td>\n",
       "      <td>45.5</td>\n",
       "      <td>5.17</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>131.61</td>\n",
       "      <td>4.52</td>\n",
       "      <td>0.90</td>\n",
       "      <td>28.79</td>\n",
       "      <td>82.67</td>\n",
       "      <td>6.84</td>\n",
       "      <td>30.02</td>\n",
       "      <td>68.62</td>\n",
       "      <td>8.76</td>\n",
       "      <td>212.50 \\r</td>\n",
       "      <td>14.0</td>\n",
       "      <td>36.7</td>\n",
       "      <td>9.63</td>\n",
       "      <td>2.4</td>\n",
       "      <td>5.5</td>\n",
       "      <td>0.3</td>\n",
       "      <td>2.0</td>\n",
       "      <td>21.5</td>\n",
       "      <td>4.5</td>\n",
       "      <td>7.75</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>149.60</td>\n",
       "      <td>4.32</td>\n",
       "      <td>0.93</td>\n",
       "      <td>29.20</td>\n",
       "      <td>82.92</td>\n",
       "      <td>9.39</td>\n",
       "      <td>32.81</td>\n",
       "      <td>76.76</td>\n",
       "      <td>8.43</td>\n",
       "      <td>247.13 \\r</td>\n",
       "      <td>12.6</td>\n",
       "      <td>18.8</td>\n",
       "      <td>12.50</td>\n",
       "      <td>2.7</td>\n",
       "      <td>65.5</td>\n",
       "      <td>1.0</td>\n",
       "      <td>27.5</td>\n",
       "      <td>237.5</td>\n",
       "      <td>60.5</td>\n",
       "      <td>5.31</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>128.08</td>\n",
       "      <td>5.07</td>\n",
       "      <td>0.93</td>\n",
       "      <td>28.21</td>\n",
       "      <td>83.18</td>\n",
       "      <td>7.53</td>\n",
       "      <td>28.10</td>\n",
       "      <td>75.64</td>\n",
       "      <td>9.53</td>\n",
       "      <td>221.75 \\r</td>\n",
       "      <td>11.8</td>\n",
       "      <td>18.5</td>\n",
       "      <td>12.07</td>\n",
       "      <td>2.2</td>\n",
       "      <td>33.8</td>\n",
       "      <td>0.8</td>\n",
       "      <td>10.0</td>\n",
       "      <td>46.5</td>\n",
       "      <td>10.0</td>\n",
       "      <td>5.87</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>...</th>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>920</th>\n",
       "      <td>142.57</td>\n",
       "      <td>4.72</td>\n",
       "      <td>0.90</td>\n",
       "      <td>29.38</td>\n",
       "      <td>83.73</td>\n",
       "      <td>8.33</td>\n",
       "      <td>29.60</td>\n",
       "      <td>78.91</td>\n",
       "      <td>8.70</td>\n",
       "      <td>241.86 \\r</td>\n",
       "      <td>11.6</td>\n",
       "      <td>14.8</td>\n",
       "      <td>12.08</td>\n",
       "      <td>3.1</td>\n",
       "      <td>73.3</td>\n",
       "      <td>1.3</td>\n",
       "      <td>24.5</td>\n",
       "      <td>136.0</td>\n",
       "      <td>20.8</td>\n",
       "      <td>5.54</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>921</th>\n",
       "      <td>129.00</td>\n",
       "      <td>5.05</td>\n",
       "      <td>0.93</td>\n",
       "      <td>28.14</td>\n",
       "      <td>83.20</td>\n",
       "      <td>7.66</td>\n",
       "      <td>28.15</td>\n",
       "      <td>76.40</td>\n",
       "      <td>9.59</td>\n",
       "      <td>221.00 \\r</td>\n",
       "      <td>12.0</td>\n",
       "      <td>20.2</td>\n",
       "      <td>11.56</td>\n",
       "      <td>2.5</td>\n",
       "      <td>19.5</td>\n",
       "      <td>0.3</td>\n",
       "      <td>6.5</td>\n",
       "      <td>25.8</td>\n",
       "      <td>5.0</td>\n",
       "      <td>6.15</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>922</th>\n",
       "      <td>141.43</td>\n",
       "      <td>4.74</td>\n",
       "      <td>0.90</td>\n",
       "      <td>29.33</td>\n",
       "      <td>83.60</td>\n",
       "      <td>8.37</td>\n",
       "      <td>29.43</td>\n",
       "      <td>79.42</td>\n",
       "      <td>8.84</td>\n",
       "      <td>243.29 \\r</td>\n",
       "      <td>12.1</td>\n",
       "      <td>14.6</td>\n",
       "      <td>13.06</td>\n",
       "      <td>2.8</td>\n",
       "      <td>94.0</td>\n",
       "      <td>1.5</td>\n",
       "      <td>34.5</td>\n",
       "      <td>171.5</td>\n",
       "      <td>23.8</td>\n",
       "      <td>5.42</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>923</th>\n",
       "      <td>141.82</td>\n",
       "      <td>4.44</td>\n",
       "      <td>0.91</td>\n",
       "      <td>28.90</td>\n",
       "      <td>83.09</td>\n",
       "      <td>9.35</td>\n",
       "      <td>29.77</td>\n",
       "      <td>78.32</td>\n",
       "      <td>9.60</td>\n",
       "      <td>279.32 \\r</td>\n",
       "      <td>12.7</td>\n",
       "      <td>18.5</td>\n",
       "      <td>12.64</td>\n",
       "      <td>3.7</td>\n",
       "      <td>89.5</td>\n",
       "      <td>2.0</td>\n",
       "      <td>31.0</td>\n",
       "      <td>210.0</td>\n",
       "      <td>46.0</td>\n",
       "      <td>5.27</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>924</th>\n",
       "      <td>144.69</td>\n",
       "      <td>4.56</td>\n",
       "      <td>0.91</td>\n",
       "      <td>29.53</td>\n",
       "      <td>83.52</td>\n",
       "      <td>7.21</td>\n",
       "      <td>31.99</td>\n",
       "      <td>70.82</td>\n",
       "      <td>8.25</td>\n",
       "      <td>229.00 \\r</td>\n",
       "      <td>13.8</td>\n",
       "      <td>18.1</td>\n",
       "      <td>11.84</td>\n",
       "      <td>2.6</td>\n",
       "      <td>35.3</td>\n",
       "      <td>0.3</td>\n",
       "      <td>15.0</td>\n",
       "      <td>132.3</td>\n",
       "      <td>29.3</td>\n",
       "      <td>5.30</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "<p>925 rows × 20 columns</p>\n",
       "</div>"
      ],
      "text/plain": [
       "       kfxs  mklz   csd   pjcd    zqd  dqzs  dlbqd    fsl    hd         mj  \\\n",
       "0    148.70  4.33  0.93  29.16  82.91  9.40  32.58  76.75  8.48  247.70 \\r   \n",
       "1    136.01  4.52  0.89  28.92  82.99  6.70  30.46  68.67  8.87  204.25 \\r   \n",
       "2    131.61  4.52  0.90  28.79  82.67  6.84  30.02  68.62  8.76  212.50 \\r   \n",
       "3    149.60  4.32  0.93  29.20  82.92  9.39  32.81  76.76  8.43  247.13 \\r   \n",
       "4    128.08  5.07  0.93  28.21  83.18  7.53  28.10  75.64  9.53  221.75 \\r   \n",
       "..      ...   ...   ...    ...    ...   ...    ...    ...   ...        ...   \n",
       "920  142.57  4.72  0.90  29.38  83.73  8.33  29.60  78.91  8.70  241.86 \\r   \n",
       "921  129.00  5.05  0.93  28.14  83.20  7.66  28.15  76.40  9.59  221.00 \\r   \n",
       "922  141.43  4.74  0.90  29.33  83.60  8.37  29.43  79.42  8.84  243.29 \\r   \n",
       "923  141.82  4.44  0.91  28.90  83.09  9.35  29.77  78.32  9.60  279.32 \\r   \n",
       "924  144.69  4.56  0.91  29.53  83.52  7.21  31.99  70.82  8.25  229.00 \\r   \n",
       "\n",
       "     scdlqd  sjhs  xscvm  xscvb  xi40  xi50  cu50  xsmj140  xsmj200  xshz  \n",
       "0      14.0  18.5  12.82    3.2  83.0   1.0  39.5    282.0     80.0  5.10  \n",
       "1      13.2  14.6  12.61    2.4  62.3   0.8  31.8    213.8     45.5  5.17  \n",
       "2      14.0  36.7   9.63    2.4   5.5   0.3   2.0     21.5      4.5  7.75  \n",
       "3      12.6  18.8  12.50    2.7  65.5   1.0  27.5    237.5     60.5  5.31  \n",
       "4      11.8  18.5  12.07    2.2  33.8   0.8  10.0     46.5     10.0  5.87  \n",
       "..      ...   ...    ...    ...   ...   ...   ...      ...      ...   ...  \n",
       "920    11.6  14.8  12.08    3.1  73.3   1.3  24.5    136.0     20.8  5.54  \n",
       "921    12.0  20.2  11.56    2.5  19.5   0.3   6.5     25.8      5.0  6.15  \n",
       "922    12.1  14.6  13.06    2.8  94.0   1.5  34.5    171.5     23.8  5.42  \n",
       "923    12.7  18.5  12.64    3.7  89.5   2.0  31.0    210.0     46.0  5.27  \n",
       "924    13.8  18.1  11.84    2.6  35.3   0.3  15.0    132.3     29.3  5.30  \n",
       "\n",
       "[925 rows x 20 columns]"
      ]
     },
     "execution_count": 5,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "data"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 6,
   "id": "miniature-withdrawal",
   "metadata": {},
   "outputs": [],
   "source": [
    "import matplotlib.pyplot as plt\n",
    "%matplotlib inline"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 7,
   "id": "blocked-latest",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "     scdlqd  sjhs  xscvm  xscvb   xi40  xi50  cu50  xsmj140  xsmj200  xshz\n",
      "5      13.1  18.6  12.36    2.8   57.5   1.3  13.8     85.5     14.3  6.02\n",
      "6      13.1  18.5  12.05    3.3   36.0   0.3  10.3     72.5     10.8  5.88\n",
      "7      12.1  14.6  13.06    2.8   94.0   1.5  34.5    171.5     23.8  5.42\n",
      "8      13.8  14.6  13.22    2.8  111.7   4.6  31.7    201.7     33.3  5.60\n",
      "9      14.0  36.7   9.63    2.4    5.5   0.3   2.0     21.5      4.5  7.75\n",
      "..      ...   ...    ...    ...    ...   ...   ...      ...      ...   ...\n",
      "920    11.6  14.8  12.08    3.1   73.3   1.3  24.5    136.0     20.8  5.54\n",
      "921    12.0  20.2  11.56    2.5   19.5   0.3   6.5     25.8      5.0  6.15\n",
      "922    12.1  14.6  13.06    2.8   94.0   1.5  34.5    171.5     23.8  5.42\n",
      "923    12.7  18.5  12.64    3.7   89.5   2.0  31.0    210.0     46.0  5.27\n",
      "924    13.8  18.1  11.84    2.6   35.3   0.3  15.0    132.3     29.3  5.30\n",
      "\n",
      "[920 rows x 10 columns]        kfxs  mklz   csd   pjcd    zqd  dqzs  dlbqd    fsl    hd\n",
      "5    117.63  4.98  0.92  28.13  81.61  8.22  26.23  78.64  9.94\n",
      "6    131.21  4.92  0.93  28.40  83.13  7.74  28.67  76.28  9.46\n",
      "7    141.43  4.75  0.90  29.32  83.61  8.37  29.44  79.31  8.83\n",
      "8    118.56  4.96  0.92  28.21  81.71  8.22  26.34  78.46  9.83\n",
      "9    129.19  4.58  0.90  28.75  82.54  6.89  29.68  68.90  8.71\n",
      "..      ...   ...   ...    ...    ...   ...    ...    ...   ...\n",
      "920  142.57  4.72  0.90  29.38  83.73  8.33  29.60  78.91  8.70\n",
      "921  129.00  5.05  0.93  28.14  83.20  7.66  28.15  76.40  9.59\n",
      "922  141.43  4.74  0.90  29.33  83.60  8.37  29.43  79.42  8.84\n",
      "923  141.82  4.44  0.91  28.90  83.09  9.35  29.77  78.32  9.60\n",
      "924  144.69  4.56  0.91  29.53  83.52  7.21  31.99  70.82  8.25\n",
      "\n",
      "[920 rows x 9 columns]\n"
     ]
    }
   ],
   "source": [
    "# x输入 y输出要预测的列\n",
    "x = data.iloc[5:,10:]\n",
    "y = data.iloc[5:,:9]\n",
    "print(x,y)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 8,
   "id": "liberal-reach",
   "metadata": {},
   "outputs": [],
   "source": [
    "# Dense第一个参数（隐藏单元数，比如10个单元的隐含层），这个是自己设定可以是10也可以是100，参数越大，拟合能力越强，但是也会有缺点。\n",
    "# input_shape即张量的shape。输入多少个字段就设置几。 input_length：代表序列长度，可以理解成有多少个样本 input_dim：代表张量的维度。\n",
    "# 激活函数 （对中间层进行激活，提高拟合能力，正是神经元的工作原理 activation=  sigmoid/relu  ）\n",
    "# model = tf.keras.Sequential([tf.keras.layers.Dense(50,input_shape=(10,),activation='sigmoid'),\n",
    "#                              tf.keras.layers.Dense(50,input_shape=(10,),activation='sigmoid'),\n",
    "#                              tf.keras.layers.Dense(50,input_shape=(10,),activation='relu'),\n",
    "#                              tf.keras.layers.Dense(50,input_shape=(10,),activation='relu'),\n",
    "#                              tf.keras.layers.Dense(50,input_shape=(10,),activation='softmax'),\n",
    "#                              tf.keras.layers.Dense(50,input_shape=(10,),activation='sigmoid'),\n",
    "#                             tf.keras.layers.Dense(9)])\n",
    "\n",
    "model = tf.keras.Sequential()\n",
    "model.add(tf.keras.layers.Flatten(input_shape = (10,)))\n",
    "model.add(tf.keras.layers.Dense(70,activation=\"sigmoid\"))\n",
    "model.add(tf.keras.layers.Dense(70,activation=\"sigmoid\"))\n",
    "model.add(tf.keras.layers.Dropout(0.2))\n",
    "model.add(tf.keras.layers.Dense(70,activation=\"sigmoid\"))\n",
    "model.add(tf.keras.layers.Dense(70,activation=\"sigmoid\"))\n",
    "model.add(tf.keras.layers.Dense(9))"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 9,
   "id": "average-kazakhstan",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Model: \"sequential\"\n",
      "_________________________________________________________________\n",
      "Layer (type)                 Output Shape              Param #   \n",
      "=================================================================\n",
      "flatten (Flatten)            (None, 10)                0         \n",
      "_________________________________________________________________\n",
      "dense (Dense)                (None, 70)                770       \n",
      "_________________________________________________________________\n",
      "dense_1 (Dense)              (None, 70)                4970      \n",
      "_________________________________________________________________\n",
      "dropout (Dropout)            (None, 70)                0         \n",
      "_________________________________________________________________\n",
      "dense_2 (Dense)              (None, 70)                4970      \n",
      "_________________________________________________________________\n",
      "dense_3 (Dense)              (None, 70)                4970      \n",
      "_________________________________________________________________\n",
      "dense_4 (Dense)              (None, 9)                 639       \n",
      "=================================================================\n",
      "Total params: 16,319\n",
      "Trainable params: 16,319\n",
      "Non-trainable params: 0\n",
      "_________________________________________________________________\n"
     ]
    }
   ],
   "source": [
    "# 中间层包含6个隐藏单元，一共有66个参数\n",
    "# 输入10个列 对应每一个单元需要十个单元，再加上一个偏置 所以有66个参数\n",
    "# x1*w1 x2*w2 x3*w3...再加一个biles  *6\n",
    "# 1*6+1偏置  7个参数\n",
    "model.summary()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 10,
   "id": "dynamic-payment",
   "metadata": {},
   "outputs": [],
   "source": [
    "# 优化器optimizer：adam是最常用的 loss：输出是一个连续的值，均方差 mean square error\n",
    "    model.compile(optimizer =tf.keras.optimizers.Adam(learning_rate=0.001),loss='mse',metrics=['accuracy'])"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 11,
   "id": "local-wallace",
   "metadata": {
    "scrolled": true
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Epoch 1/2500\n",
      "29/29 [==============================] - 1s 2ms/step - loss: 3468.6605 - accuracy: 0.0000e+00\n",
      "Epoch 2/2500\n",
      "29/29 [==============================] - 0s 2ms/step - loss: 3263.8442 - accuracy: 0.0000e+00\n",
      "Epoch 3/2500\n",
      "29/29 [==============================] - 0s 2ms/step - loss: 3107.5326 - accuracy: 0.0000e+00\n",
      "Epoch 4/2500\n",
      "29/29 [==============================] - 0s 2ms/step - loss: 2997.8965 - accuracy: 0.0000e+00\n",
      "Epoch 5/2500\n",
      "29/29 [==============================] - 0s 2ms/step - loss: 2878.0468 - accuracy: 0.0000e+00\n",
      "Epoch 6/2500\n",
      "29/29 [==============================] - 0s 2ms/step - loss: 2777.1432 - accuracy: 0.0000e+00\n",
      "Epoch 7/2500\n",
      "29/29 [==============================] - 0s 2ms/step - loss: 2655.6405 - accuracy: 0.0540\n",
      "Epoch 8/2500\n",
      "29/29 [==============================] - 0s 2ms/step - loss: 2536.2248 - accuracy: 1.0000\n",
      "Epoch 9/2500\n",
      "29/29 [==============================] - 0s 2ms/step - loss: 2442.0453 - accuracy: 1.0000\n",
      "Epoch 10/2500\n",
      "29/29 [==============================] - 0s 1ms/step - loss: 2347.2524 - accuracy: 1.0000\n",
      "Epoch 11/2500\n",
      "29/29 [==============================] - 0s 1ms/step - loss: 2244.4270 - accuracy: 1.0000\n",
      "Epoch 12/2500\n",
      "29/29 [==============================] - 0s 1ms/step - loss: 2147.4014 - accuracy: 1.0000\n",
      "Epoch 13/2500\n",
      "29/29 [==============================] - 0s 1ms/step - loss: 2069.9870 - accuracy: 1.0000\n",
      "Epoch 14/2500\n",
      "29/29 [==============================] - 0s 1ms/step - loss: 1984.7674 - accuracy: 1.0000\n",
      "Epoch 15/2500\n",
      "29/29 [==============================] - 0s 1ms/step - loss: 1916.3590 - accuracy: 1.0000\n",
      "Epoch 16/2500\n",
      "29/29 [==============================] - 0s 1ms/step - loss: 1833.5638 - accuracy: 1.0000\n",
      "Epoch 17/2500\n",
      "29/29 [==============================] - 0s 1ms/step - loss: 1761.6612 - accuracy: 1.0000\n",
      "Epoch 18/2500\n",
      "29/29 [==============================] - 0s 1ms/step - loss: 1688.3714 - accuracy: 1.0000\n",
      "Epoch 19/2500\n",
      "29/29 [==============================] - 0s 1ms/step - loss: 1619.6114 - accuracy: 1.0000\n",
      "Epoch 20/2500\n",
      "29/29 [==============================] - 0s 1ms/step - loss: 1540.9267 - accuracy: 1.0000\n",
      "Epoch 21/2500\n",
      "29/29 [==============================] - 0s 1ms/step - loss: 1486.0452 - accuracy: 1.0000\n",
      "Epoch 22/2500\n",
      "29/29 [==============================] - 0s 1ms/step - loss: 1413.2825 - accuracy: 1.0000\n",
      "Epoch 23/2500\n",
      "29/29 [==============================] - 0s 1ms/step - loss: 1356.4325 - accuracy: 1.0000\n",
      "Epoch 24/2500\n",
      "29/29 [==============================] - 0s 1ms/step - loss: 1291.6454 - accuracy: 1.0000\n",
      "Epoch 25/2500\n",
      "29/29 [==============================] - 0s 1ms/step - loss: 1241.3610 - accuracy: 1.0000\n",
      "Epoch 26/2500\n",
      "29/29 [==============================] - 0s 1ms/step - loss: 1187.5786 - accuracy: 1.0000\n",
      "Epoch 27/2500\n",
      "29/29 [==============================] - 0s 1ms/step - loss: 1140.7643 - accuracy: 1.0000\n",
      "Epoch 28/2500\n",
      "29/29 [==============================] - 0s 1ms/step - loss: 1083.8859 - accuracy: 1.0000\n",
      "Epoch 29/2500\n",
      "29/29 [==============================] - 0s 1ms/step - loss: 1040.2554 - accuracy: 1.0000\n",
      "Epoch 30/2500\n",
      "29/29 [==============================] - 0s 1ms/step - loss: 1008.6069 - accuracy: 1.0000\n",
      "Epoch 31/2500\n",
      "29/29 [==============================] - 0s 1ms/step - loss: 961.5698 - accuracy: 1.0000\n",
      "Epoch 32/2500\n",
      "29/29 [==============================] - 0s 1ms/step - loss: 914.8933 - accuracy: 1.0000\n",
      "Epoch 33/2500\n",
      "29/29 [==============================] - 0s 1ms/step - loss: 874.2033 - accuracy: 1.0000\n",
      "Epoch 34/2500\n",
      "29/29 [==============================] - 0s 1ms/step - loss: 836.5776 - accuracy: 1.0000\n",
      "Epoch 35/2500\n",
      "29/29 [==============================] - 0s 1ms/step - loss: 802.3901 - accuracy: 1.0000\n",
      "Epoch 36/2500\n",
      "29/29 [==============================] - 0s 1ms/step - loss: 756.6937 - accuracy: 1.0000\n",
      "Epoch 37/2500\n",
      "29/29 [==============================] - 0s 1ms/step - loss: 735.1350 - accuracy: 1.0000\n",
      "Epoch 38/2500\n",
      "29/29 [==============================] - 0s 1ms/step - loss: 700.3714 - accuracy: 1.0000\n",
      "Epoch 39/2500\n",
      "29/29 [==============================] - 0s 1ms/step - loss: 666.6863 - accuracy: 1.0000\n",
      "Epoch 40/2500\n",
      "29/29 [==============================] - 0s 1ms/step - loss: 644.8289 - accuracy: 1.0000\n",
      "Epoch 41/2500\n",
      "29/29 [==============================] - 0s 1ms/step - loss: 608.8069 - accuracy: 1.0000\n",
      "Epoch 42/2500\n",
      "29/29 [==============================] - 0s 1ms/step - loss: 579.3678 - accuracy: 1.0000\n",
      "Epoch 43/2500\n",
      "29/29 [==============================] - 0s 1ms/step - loss: 558.3796 - accuracy: 1.0000\n",
      "Epoch 44/2500\n",
      "29/29 [==============================] - 0s 1ms/step - loss: 534.8604 - accuracy: 1.0000\n",
      "Epoch 45/2500\n",
      "29/29 [==============================] - 0s 1ms/step - loss: 504.3339 - accuracy: 1.0000\n",
      "Epoch 46/2500\n",
      "29/29 [==============================] - 0s 1ms/step - loss: 484.4809 - accuracy: 1.0000\n",
      "Epoch 47/2500\n",
      "29/29 [==============================] - 0s 1ms/step - loss: 466.2792 - accuracy: 1.0000\n",
      "Epoch 48/2500\n",
      "29/29 [==============================] - 0s 1ms/step - loss: 442.9718 - accuracy: 1.0000\n",
      "Epoch 49/2500\n",
      "29/29 [==============================] - 0s 1ms/step - loss: 423.7497 - accuracy: 1.0000\n",
      "Epoch 50/2500\n",
      "29/29 [==============================] - 0s 1ms/step - loss: 404.1606 - accuracy: 1.0000\n",
      "Epoch 51/2500\n",
      "29/29 [==============================] - 0s 1ms/step - loss: 386.1112 - accuracy: 1.0000\n",
      "Epoch 52/2500\n",
      "29/29 [==============================] - 0s 1ms/step - loss: 366.9739 - accuracy: 1.0000\n",
      "Epoch 53/2500\n",
      "29/29 [==============================] - 0s 1ms/step - loss: 353.6525 - accuracy: 1.0000\n",
      "Epoch 54/2500\n",
      "29/29 [==============================] - 0s 1ms/step - loss: 334.0759 - accuracy: 1.0000\n",
      "Epoch 55/2500\n",
      "29/29 [==============================] - 0s 1ms/step - loss: 314.0919 - accuracy: 1.0000\n",
      "Epoch 56/2500\n",
      "29/29 [==============================] - 0s 1ms/step - loss: 305.3529 - accuracy: 1.0000\n",
      "Epoch 57/2500\n",
      "29/29 [==============================] - 0s 1ms/step - loss: 286.9712 - accuracy: 1.0000\n",
      "Epoch 58/2500\n",
      "29/29 [==============================] - 0s 1ms/step - loss: 274.2630 - accuracy: 1.0000\n",
      "Epoch 59/2500\n",
      "29/29 [==============================] - 0s 1ms/step - loss: 262.5894 - accuracy: 1.0000\n",
      "Epoch 60/2500\n",
      "29/29 [==============================] - 0s 1ms/step - loss: 249.0858 - accuracy: 1.0000\n",
      "Epoch 61/2500\n",
      "29/29 [==============================] - 0s 1ms/step - loss: 237.2753 - accuracy: 1.0000\n",
      "Epoch 62/2500\n",
      "29/29 [==============================] - 0s 1ms/step - loss: 225.3533 - accuracy: 1.0000\n",
      "Epoch 63/2500\n",
      "29/29 [==============================] - 0s 1ms/step - loss: 215.2861 - accuracy: 1.0000\n",
      "Epoch 64/2500\n",
      "29/29 [==============================] - 0s 1ms/step - loss: 199.1133 - accuracy: 1.0000\n",
      "Epoch 65/2500\n",
      "29/29 [==============================] - 0s 1ms/step - loss: 195.0534 - accuracy: 1.0000\n",
      "Epoch 66/2500\n",
      "29/29 [==============================] - 0s 1ms/step - loss: 187.4995 - accuracy: 1.0000\n",
      "Epoch 67/2500\n",
      "29/29 [==============================] - 0s 1ms/step - loss: 175.2899 - accuracy: 1.0000\n",
      "Epoch 68/2500\n",
      "29/29 [==============================] - 0s 1ms/step - loss: 165.7659 - accuracy: 1.0000\n",
      "Epoch 69/2500\n",
      "29/29 [==============================] - 0s 1ms/step - loss: 156.4960 - accuracy: 1.0000\n",
      "Epoch 70/2500\n",
      "29/29 [==============================] - 0s 1ms/step - loss: 153.3853 - accuracy: 1.0000\n",
      "Epoch 71/2500\n",
      "29/29 [==============================] - 0s 1ms/step - loss: 144.5978 - accuracy: 1.0000\n",
      "Epoch 72/2500\n",
      "29/29 [==============================] - 0s 1ms/step - loss: 135.1114 - accuracy: 1.0000\n",
      "Epoch 73/2500\n",
      "29/29 [==============================] - 0s 1ms/step - loss: 128.3443 - accuracy: 1.0000\n",
      "Epoch 74/2500\n",
      "29/29 [==============================] - 0s 1ms/step - loss: 122.2422 - accuracy: 1.0000\n",
      "Epoch 75/2500\n",
      "29/29 [==============================] - 0s 1ms/step - loss: 116.2146 - accuracy: 1.0000\n",
      "Epoch 76/2500\n",
      "29/29 [==============================] - 0s 1ms/step - loss: 106.7448 - accuracy: 1.0000\n",
      "Epoch 77/2500\n",
      "29/29 [==============================] - 0s 1ms/step - loss: 104.4143 - accuracy: 1.0000\n",
      "Epoch 78/2500\n",
      "29/29 [==============================] - 0s 1ms/step - loss: 98.8212 - accuracy: 1.0000\n",
      "Epoch 79/2500\n",
      "29/29 [==============================] - 0s 1ms/step - loss: 93.2295 - accuracy: 1.0000\n",
      "Epoch 80/2500\n"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "29/29 [==============================] - 0s 1ms/step - loss: 88.2525 - accuracy: 1.0000\n",
      "Epoch 81/2500\n",
      "29/29 [==============================] - 0s 1ms/step - loss: 85.7097 - accuracy: 1.0000\n",
      "Epoch 82/2500\n",
      "29/29 [==============================] - 0s 1ms/step - loss: 79.3746 - accuracy: 1.0000\n",
      "Epoch 83/2500\n",
      "29/29 [==============================] - 0s 1ms/step - loss: 76.8704 - accuracy: 1.0000\n",
      "Epoch 84/2500\n",
      "29/29 [==============================] - 0s 1ms/step - loss: 70.4847 - accuracy: 1.0000\n",
      "Epoch 85/2500\n",
      "29/29 [==============================] - 0s 1ms/step - loss: 67.4243 - accuracy: 1.0000\n",
      "Epoch 86/2500\n",
      "29/29 [==============================] - 0s 1ms/step - loss: 65.5492 - accuracy: 1.0000\n",
      "Epoch 87/2500\n",
      "29/29 [==============================] - 0s 1ms/step - loss: 58.4562 - accuracy: 1.0000\n",
      "Epoch 88/2500\n",
      "29/29 [==============================] - 0s 1ms/step - loss: 57.3631 - accuracy: 1.0000\n",
      "Epoch 89/2500\n",
      "29/29 [==============================] - 0s 1ms/step - loss: 54.5979 - accuracy: 1.0000\n",
      "Epoch 90/2500\n",
      "29/29 [==============================] - 0s 1ms/step - loss: 51.5172 - accuracy: 1.0000\n",
      "Epoch 91/2500\n",
      "29/29 [==============================] - 0s 1ms/step - loss: 48.5939 - accuracy: 1.0000\n",
      "Epoch 92/2500\n",
      "29/29 [==============================] - 0s 1ms/step - loss: 46.6539 - accuracy: 1.0000\n",
      "Epoch 93/2500\n",
      "29/29 [==============================] - 0s 1ms/step - loss: 44.7931 - accuracy: 1.0000\n",
      "Epoch 94/2500\n",
      "29/29 [==============================] - 0s 1ms/step - loss: 39.8920 - accuracy: 1.0000\n",
      "Epoch 95/2500\n",
      "29/29 [==============================] - 0s 1ms/step - loss: 38.4564 - accuracy: 1.0000\n",
      "Epoch 96/2500\n",
      "29/29 [==============================] - 0s 1ms/step - loss: 37.3356 - accuracy: 1.0000\n",
      "Epoch 97/2500\n",
      "29/29 [==============================] - 0s 1ms/step - loss: 32.9565 - accuracy: 1.0000\n",
      "Epoch 98/2500\n",
      "29/29 [==============================] - 0s 1ms/step - loss: 32.8651 - accuracy: 1.0000\n",
      "Epoch 99/2500\n",
      "29/29 [==============================] - 0s 1ms/step - loss: 29.3497 - accuracy: 1.0000\n",
      "Epoch 100/2500\n",
      "29/29 [==============================] - 0s 1ms/step - loss: 29.0613 - accuracy: 1.0000\n",
      "Epoch 101/2500\n",
      "29/29 [==============================] - 0s 1ms/step - loss: 27.5674 - accuracy: 1.0000\n",
      "Epoch 102/2500\n",
      "29/29 [==============================] - 0s 1ms/step - loss: 25.3620 - accuracy: 1.0000\n",
      "Epoch 103/2500\n",
      "29/29 [==============================] - 0s 1ms/step - loss: 25.5525 - accuracy: 1.0000\n",
      "Epoch 104/2500\n",
      "29/29 [==============================] - 0s 1ms/step - loss: 22.9916 - accuracy: 1.0000\n",
      "Epoch 105/2500\n",
      "29/29 [==============================] - 0s 1ms/step - loss: 23.3229 - accuracy: 1.0000\n",
      "Epoch 106/2500\n",
      "29/29 [==============================] - 0s 1ms/step - loss: 19.9549 - accuracy: 1.0000\n",
      "Epoch 107/2500\n",
      "29/29 [==============================] - 0s 1ms/step - loss: 21.2564 - accuracy: 1.0000\n",
      "Epoch 108/2500\n",
      "29/29 [==============================] - 0s 1ms/step - loss: 19.0626 - accuracy: 1.0000\n",
      "Epoch 109/2500\n",
      "29/29 [==============================] - 0s 1ms/step - loss: 18.1387 - accuracy: 1.0000\n",
      "Epoch 110/2500\n",
      "29/29 [==============================] - 0s 1ms/step - loss: 17.3981 - accuracy: 1.0000\n",
      "Epoch 111/2500\n",
      "29/29 [==============================] - 0s 1ms/step - loss: 17.3641 - accuracy: 1.0000\n",
      "Epoch 112/2500\n",
      "29/29 [==============================] - 0s 1ms/step - loss: 16.2288 - accuracy: 1.0000\n",
      "Epoch 113/2500\n",
      "29/29 [==============================] - 0s 1ms/step - loss: 15.1144 - accuracy: 1.0000\n",
      "Epoch 114/2500\n",
      "29/29 [==============================] - 0s 1ms/step - loss: 15.0208 - accuracy: 1.0000\n",
      "Epoch 115/2500\n",
      "29/29 [==============================] - 0s 1ms/step - loss: 14.2870 - accuracy: 1.0000\n",
      "Epoch 116/2500\n",
      "29/29 [==============================] - 0s 1ms/step - loss: 13.8642 - accuracy: 1.0000\n",
      "Epoch 117/2500\n",
      "29/29 [==============================] - 0s 1ms/step - loss: 13.5131 - accuracy: 1.0000\n",
      "Epoch 118/2500\n",
      "29/29 [==============================] - 0s 1ms/step - loss: 12.5624 - accuracy: 1.0000\n",
      "Epoch 119/2500\n",
      "29/29 [==============================] - 0s 1ms/step - loss: 12.7304 - accuracy: 1.0000\n",
      "Epoch 120/2500\n",
      "29/29 [==============================] - 0s 1ms/step - loss: 13.0939 - accuracy: 1.0000\n",
      "Epoch 121/2500\n",
      "29/29 [==============================] - 0s 1ms/step - loss: 11.5240 - accuracy: 1.0000\n",
      "Epoch 122/2500\n",
      "29/29 [==============================] - 0s 1ms/step - loss: 11.8176 - accuracy: 1.0000\n",
      "Epoch 123/2500\n",
      "29/29 [==============================] - 0s 1ms/step - loss: 11.4494 - accuracy: 1.0000\n",
      "Epoch 124/2500\n",
      "29/29 [==============================] - 0s 1ms/step - loss: 11.5964 - accuracy: 1.0000\n",
      "Epoch 125/2500\n",
      "29/29 [==============================] - 0s 1ms/step - loss: 10.8768 - accuracy: 1.0000\n",
      "Epoch 126/2500\n",
      "29/29 [==============================] - 0s 1ms/step - loss: 10.8391 - accuracy: 1.0000\n",
      "Epoch 127/2500\n",
      "29/29 [==============================] - 0s 1ms/step - loss: 11.1447 - accuracy: 1.0000\n",
      "Epoch 128/2500\n",
      "29/29 [==============================] - 0s 1ms/step - loss: 9.4024 - accuracy: 1.0000\n",
      "Epoch 129/2500\n",
      "29/29 [==============================] - 0s 1ms/step - loss: 10.2854 - accuracy: 1.0000\n",
      "Epoch 130/2500\n",
      "29/29 [==============================] - 0s 1ms/step - loss: 9.7240 - accuracy: 1.0000\n",
      "Epoch 131/2500\n",
      "29/29 [==============================] - 0s 1ms/step - loss: 9.9249 - accuracy: 1.0000\n",
      "Epoch 132/2500\n",
      "29/29 [==============================] - 0s 1ms/step - loss: 9.8968 - accuracy: 1.0000\n",
      "Epoch 133/2500\n",
      "29/29 [==============================] - 0s 1ms/step - loss: 9.9003 - accuracy: 1.0000\n",
      "Epoch 134/2500\n",
      "29/29 [==============================] - 0s 1ms/step - loss: 9.6754 - accuracy: 1.0000\n",
      "Epoch 135/2500\n",
      "29/29 [==============================] - 0s 1ms/step - loss: 9.6248 - accuracy: 1.0000\n",
      "Epoch 136/2500\n",
      "29/29 [==============================] - 0s 1ms/step - loss: 8.6277 - accuracy: 1.0000\n",
      "Epoch 137/2500\n",
      "29/29 [==============================] - 0s 1ms/step - loss: 9.6863 - accuracy: 1.0000\n",
      "Epoch 138/2500\n",
      "29/29 [==============================] - 0s 1ms/step - loss: 8.9821 - accuracy: 1.0000\n",
      "Epoch 139/2500\n",
      "29/29 [==============================] - 0s 1ms/step - loss: 9.6704 - accuracy: 1.0000\n",
      "Epoch 140/2500\n",
      "29/29 [==============================] - 0s 1ms/step - loss: 9.6767 - accuracy: 1.0000\n",
      "Epoch 141/2500\n",
      "29/29 [==============================] - 0s 1ms/step - loss: 8.7700 - accuracy: 1.0000\n",
      "Epoch 142/2500\n",
      "29/29 [==============================] - 0s 1ms/step - loss: 9.2912 - accuracy: 1.0000\n",
      "Epoch 143/2500\n",
      "29/29 [==============================] - 0s 1ms/step - loss: 9.1322 - accuracy: 1.0000\n",
      "Epoch 144/2500\n",
      "29/29 [==============================] - 0s 1ms/step - loss: 8.8868 - accuracy: 1.0000\n",
      "Epoch 145/2500\n",
      "29/29 [==============================] - 0s 1ms/step - loss: 9.1131 - accuracy: 1.0000\n",
      "Epoch 146/2500\n",
      "29/29 [==============================] - 0s 1ms/step - loss: 8.6257 - accuracy: 1.0000\n",
      "Epoch 147/2500\n",
      "29/29 [==============================] - 0s 1ms/step - loss: 8.6258 - accuracy: 1.0000\n",
      "Epoch 148/2500\n",
      "29/29 [==============================] - 0s 1ms/step - loss: 9.1575 - accuracy: 1.0000\n",
      "Epoch 149/2500\n",
      "29/29 [==============================] - 0s 1ms/step - loss: 8.9057 - accuracy: 1.0000\n",
      "Epoch 150/2500\n",
      "29/29 [==============================] - 0s 1ms/step - loss: 8.9369 - accuracy: 1.0000\n",
      "Epoch 151/2500\n",
      "29/29 [==============================] - 0s 1ms/step - loss: 8.6981 - accuracy: 1.0000\n",
      "Epoch 152/2500\n",
      "29/29 [==============================] - 0s 1ms/step - loss: 8.4834 - accuracy: 1.0000\n",
      "Epoch 153/2500\n",
      "29/29 [==============================] - 0s 1ms/step - loss: 8.8584 - accuracy: 1.0000\n",
      "Epoch 154/2500\n",
      "29/29 [==============================] - 0s 1ms/step - loss: 8.6801 - accuracy: 1.0000\n",
      "Epoch 155/2500\n",
      "29/29 [==============================] - 0s 1ms/step - loss: 9.1902 - accuracy: 1.0000\n",
      "Epoch 156/2500\n",
      "29/29 [==============================] - 0s 1ms/step - loss: 8.5484 - accuracy: 1.0000\n",
      "Epoch 157/2500\n",
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     ]
    },
    {
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     ]
    },
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    },
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     ]
    },
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     ]
    },
    {
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    },
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     ]
    },
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    },
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      "Epoch 1988/2500\n"
     ]
    },
    {
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     "output_type": "stream",
     "text": [
      "29/29 [==============================] - 0s 1ms/step - loss: 0.2995 - accuracy: 1.0000\n",
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     ]
    },
    {
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     "text": [
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    },
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      "29/29 [==============================] - 0s 1ms/step - loss: 0.3007 - accuracy: 1.0000\n",
      "Epoch 2444/2500\n",
      "29/29 [==============================] - 0s 1ms/step - loss: 0.3112 - accuracy: 1.0000\n",
      "Epoch 2445/2500\n",
      "29/29 [==============================] - 0s 1ms/step - loss: 0.3486 - accuracy: 1.0000\n",
      "Epoch 2446/2500\n",
      "29/29 [==============================] - 0s 1ms/step - loss: 0.4769 - accuracy: 1.0000\n",
      "Epoch 2447/2500\n",
      "29/29 [==============================] - 0s 1ms/step - loss: 0.3317 - accuracy: 1.0000\n",
      "Epoch 2448/2500\n",
      "29/29 [==============================] - 0s 1ms/step - loss: 0.3914 - accuracy: 1.0000\n",
      "Epoch 2449/2500\n",
      "29/29 [==============================] - 0s 1ms/step - loss: 0.3479 - accuracy: 1.0000\n",
      "Epoch 2450/2500\n",
      "29/29 [==============================] - 0s 1ms/step - loss: 0.3035 - accuracy: 1.0000\n",
      "Epoch 2451/2500\n",
      "29/29 [==============================] - 0s 1ms/step - loss: 0.2767 - accuracy: 1.0000\n",
      "Epoch 2452/2500\n",
      "29/29 [==============================] - 0s 1ms/step - loss: 0.3053 - accuracy: 1.0000\n",
      "Epoch 2453/2500\n",
      "29/29 [==============================] - 0s 1ms/step - loss: 0.3005 - accuracy: 1.0000\n",
      "Epoch 2454/2500\n",
      "29/29 [==============================] - 0s 1ms/step - loss: 0.3030 - accuracy: 1.0000\n",
      "Epoch 2455/2500\n",
      "29/29 [==============================] - 0s 1ms/step - loss: 0.2819 - accuracy: 1.0000\n",
      "Epoch 2456/2500\n",
      "29/29 [==============================] - 0s 1ms/step - loss: 0.2801 - accuracy: 1.0000\n",
      "Epoch 2457/2500\n",
      "29/29 [==============================] - 0s 1ms/step - loss: 0.3276 - accuracy: 1.0000\n",
      "Epoch 2458/2500\n",
      "29/29 [==============================] - 0s 1ms/step - loss: 0.3131 - accuracy: 1.0000\n",
      "Epoch 2459/2500\n",
      "29/29 [==============================] - 0s 1ms/step - loss: 0.2975 - accuracy: 1.0000\n",
      "Epoch 2460/2500\n",
      "29/29 [==============================] - 0s 1ms/step - loss: 0.3718 - accuracy: 1.0000\n",
      "Epoch 2461/2500\n",
      "29/29 [==============================] - 0s 1ms/step - loss: 0.5276 - accuracy: 1.0000\n",
      "Epoch 2462/2500\n"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "29/29 [==============================] - 0s 1ms/step - loss: 0.4559 - accuracy: 1.0000\n",
      "Epoch 2463/2500\n",
      "29/29 [==============================] - 0s 1ms/step - loss: 0.3266 - accuracy: 1.0000\n",
      "Epoch 2464/2500\n",
      "29/29 [==============================] - 0s 1ms/step - loss: 0.3510 - accuracy: 1.0000\n",
      "Epoch 2465/2500\n",
      "29/29 [==============================] - 0s 1ms/step - loss: 0.3817 - accuracy: 1.0000\n",
      "Epoch 2466/2500\n",
      "29/29 [==============================] - 0s 1ms/step - loss: 0.3220 - accuracy: 1.0000\n",
      "Epoch 2467/2500\n",
      "29/29 [==============================] - 0s 1ms/step - loss: 0.3346 - accuracy: 1.0000\n",
      "Epoch 2468/2500\n",
      "29/29 [==============================] - 0s 1ms/step - loss: 0.3395 - accuracy: 1.0000\n",
      "Epoch 2469/2500\n",
      "29/29 [==============================] - 0s 1ms/step - loss: 0.6281 - accuracy: 1.0000\n",
      "Epoch 2470/2500\n",
      "29/29 [==============================] - 0s 1ms/step - loss: 0.4767 - accuracy: 1.0000\n",
      "Epoch 2471/2500\n",
      "29/29 [==============================] - 0s 1ms/step - loss: 0.3010 - accuracy: 1.0000\n",
      "Epoch 2472/2500\n",
      "29/29 [==============================] - 0s 1ms/step - loss: 0.3199 - accuracy: 1.0000\n",
      "Epoch 2473/2500\n",
      "29/29 [==============================] - 0s 1ms/step - loss: 0.3103 - accuracy: 1.0000\n",
      "Epoch 2474/2500\n",
      "29/29 [==============================] - 0s 1ms/step - loss: 0.3064 - accuracy: 1.0000\n",
      "Epoch 2475/2500\n",
      "29/29 [==============================] - 0s 1ms/step - loss: 0.2929 - accuracy: 1.0000\n",
      "Epoch 2476/2500\n",
      "29/29 [==============================] - 0s 1ms/step - loss: 0.3153 - accuracy: 1.0000\n",
      "Epoch 2477/2500\n",
      "29/29 [==============================] - 0s 1ms/step - loss: 0.2818 - accuracy: 1.0000\n",
      "Epoch 2478/2500\n",
      "29/29 [==============================] - 0s 1ms/step - loss: 0.3175 - accuracy: 1.0000\n",
      "Epoch 2479/2500\n",
      "29/29 [==============================] - 0s 1ms/step - loss: 0.3045 - accuracy: 1.0000\n",
      "Epoch 2480/2500\n",
      "29/29 [==============================] - 0s 1ms/step - loss: 0.2758 - accuracy: 1.0000\n",
      "Epoch 2481/2500\n",
      "29/29 [==============================] - 0s 1ms/step - loss: 0.2957 - accuracy: 1.0000\n",
      "Epoch 2482/2500\n",
      "29/29 [==============================] - 0s 1ms/step - loss: 0.2731 - accuracy: 1.0000\n",
      "Epoch 2483/2500\n",
      "29/29 [==============================] - 0s 1ms/step - loss: 0.2997 - accuracy: 1.0000\n",
      "Epoch 2484/2500\n",
      "29/29 [==============================] - 0s 1ms/step - loss: 0.2840 - accuracy: 1.0000\n",
      "Epoch 2485/2500\n",
      "29/29 [==============================] - 0s 1ms/step - loss: 0.2993 - accuracy: 1.0000\n",
      "Epoch 2486/2500\n",
      "29/29 [==============================] - 0s 1ms/step - loss: 0.2936 - accuracy: 1.0000\n",
      "Epoch 2487/2500\n",
      "29/29 [==============================] - 0s 1ms/step - loss: 0.3516 - accuracy: 1.0000\n",
      "Epoch 2488/2500\n",
      "29/29 [==============================] - 0s 1ms/step - loss: 0.4047 - accuracy: 1.0000\n",
      "Epoch 2489/2500\n",
      "29/29 [==============================] - 0s 1ms/step - loss: 0.3480 - accuracy: 1.0000\n",
      "Epoch 2490/2500\n",
      "29/29 [==============================] - 0s 1ms/step - loss: 0.3165 - accuracy: 1.0000\n",
      "Epoch 2491/2500\n",
      "29/29 [==============================] - 0s 1ms/step - loss: 0.2631 - accuracy: 1.0000\n",
      "Epoch 2492/2500\n",
      "29/29 [==============================] - 0s 1ms/step - loss: 0.2787 - accuracy: 1.0000\n",
      "Epoch 2493/2500\n",
      "29/29 [==============================] - 0s 1ms/step - loss: 0.2857 - accuracy: 1.0000\n",
      "Epoch 2494/2500\n",
      "29/29 [==============================] - 0s 1ms/step - loss: 0.3055 - accuracy: 1.0000\n",
      "Epoch 2495/2500\n",
      "29/29 [==============================] - 0s 1ms/step - loss: 0.3122 - accuracy: 1.0000\n",
      "Epoch 2496/2500\n",
      "29/29 [==============================] - 0s 1ms/step - loss: 0.2557 - accuracy: 1.0000\n",
      "Epoch 2497/2500\n",
      "29/29 [==============================] - 0s 1ms/step - loss: 0.3051 - accuracy: 1.0000\n",
      "Epoch 2498/2500\n",
      "29/29 [==============================] - 0s 1ms/step - loss: 0.2752 - accuracy: 1.0000\n",
      "Epoch 2499/2500\n",
      "29/29 [==============================] - 0s 1ms/step - loss: 0.2998 - accuracy: 1.0000\n",
      "Epoch 2500/2500\n",
      "29/29 [==============================] - 0s 1ms/step - loss: 0.3210 - accuracy: 1.0000\n"
     ]
    },
    {
     "data": {
      "text/plain": [
       "<tensorflow.python.keras.callbacks.History at 0x7fcf987ca780>"
      ]
     },
     "execution_count": 11,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# epochs 训练多少次\n",
    "model.fit(x,y,epochs = 2500)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 12,
   "id": "cross-generator",
   "metadata": {},
   "outputs": [],
   "source": [
    "# [预测哪些值，输入哪些列]\n",
    "predict = data.iloc[0:10,10:]"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 14,
   "id": "wound-cardiff",
   "metadata": {
    "scrolled": true
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "[[149.99626      4.3148923    0.9277276   29.193117    82.96903\n",
      "    9.352894    32.88993     76.824875     8.409602  ]\n",
      " [131.41031      4.5567174    0.8968622   28.790495    82.6117\n",
      "    6.841337    29.950144    69.06488      8.690593  ]\n",
      " [131.61816      4.5753326    0.90040416  28.808096    82.581474\n",
      "    6.8611145   30.009367    69.06802      8.682448  ]\n",
      " [150.00934      4.31519      0.92771083  29.197725    82.96114\n",
      "    9.355686    32.898033    76.832306     8.410463  ]\n",
      " [129.73027      5.0042734    0.9301428   28.21684     83.211914\n",
      "    7.6737223   28.390844    76.30825      9.53271   ]\n",
      " [118.10168      4.9689384    0.921059    28.13221     81.63447\n",
      "    8.200066    26.329994    78.55208      9.863294  ]\n",
      " [129.82297      4.997746     0.9291113   28.243685    83.16885\n",
      "    7.6770997   28.39477     76.34226      9.507912  ]\n",
      " [141.70845      4.7423058    0.8990582   29.300035    83.71868\n",
      "    8.3803215   29.472578    79.17911      8.727962  ]\n",
      " [118.2633       4.9686303    0.92058927  28.158398    81.69016\n",
      "    8.203298    26.33803     78.60058      9.857634  ]\n",
      " [131.61816      4.5753326    0.90040416  28.808096    82.581474\n",
      "    6.8611145   30.009367    69.06802      8.682448  ]]\n"
     ]
    }
   ],
   "source": [
    "predict_data = model.predict(predict)\n",
    "print(predict_data)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 15,
   "id": "capital-teacher",
   "metadata": {},
   "outputs": [],
   "source": [
    "true_data = data.iloc[0:10,:9]"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 16,
   "id": "geological-series",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "     kfxs  mklz   csd   pjcd    zqd  dqzs  dlbqd    fsl    hd\n",
      "0  148.70  4.33  0.93  29.16  82.91  9.40  32.58  76.75  8.48\n",
      "1  136.01  4.52  0.89  28.92  82.99  6.70  30.46  68.67  8.87\n",
      "2  131.61  4.52  0.90  28.79  82.67  6.84  30.02  68.62  8.76\n",
      "3  149.60  4.32  0.93  29.20  82.92  9.39  32.81  76.76  8.43\n",
      "4  128.08  5.07  0.93  28.21  83.18  7.53  28.10  75.64  9.53\n",
      "5  117.63  4.98  0.92  28.13  81.61  8.22  26.23  78.64  9.94\n",
      "6  131.21  4.92  0.93  28.40  83.13  7.74  28.67  76.28  9.46\n",
      "7  141.43  4.75  0.90  29.32  83.61  8.37  29.44  79.31  8.83\n",
      "8  118.56  4.96  0.92  28.21  81.71  8.22  26.34  78.46  9.83\n",
      "9  129.19  4.58  0.90  28.75  82.54  6.89  29.68  68.90  8.71\n"
     ]
    }
   ],
   "source": [
    "print(true_data)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "id": "unlike-viking",
   "metadata": {},
   "outputs": [],
   "source": []
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "id": "surrounded-triple",
   "metadata": {},
   "outputs": [],
   "source": []
  }
 ],
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